Action research data analysisPresentation Transcript
Action ReseachData AnalysisGroup 5
The Role Of Data AnalysisAnd InterpretationPresenter: Dinh Quoc Minh Dang
IntroductionYou will reach a point in the research process where you will want tosummarize what you have learned and what you think it means for yourstudents.You will want to share you findings without having to share all of your dataand use these findings to identify what will happen next in the action researchprocess.This critical component of the action research process is called dataanalysis and interpretation, and it needs to be carefully thought out.
1. Data AnalysisData analysis is an attempt by the teacher researcher to summarizecollected data in a dependable and accurate manner. The type of datayou collect will determine the data analysis techniques you will use. For example, if you collect narrative, descriptive, and nonnumerical data, such as field notes from observations or interviews, questionnaires, or pictures, qualitative data analysis will be best suited for your needs.
1. Data AnalysisBenefits: Data analysis helps in structuring the findings from different sources of data collection and provides a meaningful base for critical decisions. It helps in keeping human bias away from research conclusion with the help of proper statistical treatmentAnalysis involves exploring data to uncover trends and patterns, as well asrelationships among different variables
2. Data InterpretationData analysis, however, is only one step in the process; researchers mustthen interpret the findings of their analyses, reporting them in such a way thatothers can make sense of the results and put them to useData interpretation is an attempt by the researcher to find meaning inthe data, to answer the “So what?” question in terms of the implications ofthe study’s findings. Put simply, analysis involves summarizing what’s in thedata, whereas interpretation involves making sense of finding meaning in thatdata.
2. Data InterpretationData interpretation means explaining the trends, patterns and relationshipsthat emerge in the analysisInterpretation should focus on providing clear explanations and, whereapplicable, recommended actions based on the data and analysis.
Sum-upData analysis and interpretation are critical stages in the action researchprocess that require the teacher researcher to both know and understand thedata.When analyzing and interpreting data, challenge yourself to explore everypossible angle and try to find patterns and seek out new understandingamong the data.
II. Principles Of ActionResearch’s Data Analysis AndInterpretation Presenter: Minh Sang
3. Data AnalysisAnalysis of data is a process of inspecting, cleaning, transforming, andmodeling data with the goal of highlighting useful information, suggestingconclusions, and supporting decision making.Data analysis has multiple facets and approaches, encompassing diversetechniques under a variety of names, in different business, science, andsocial science domains.
4. Synthesis The process of drawing together concepts, ideas, objects and other qualitative data in new configurations, or to create something entirely new. Combining multiple elements together to create a new, complex ‘thing’ is what the technique of synthesis is all about. Similar in some respects to aggregation, synthesis typically deals with non-numeric data.
4. Synthesis Synthesis is often undertaken towards the end of an analytic process as the reverse of deconstruction. So where we might begin by breaking down data into its component parts and examining them; we often end by recombining those components in new ways. Note, however, that synthesis can also form part of an exploration and is one of the fundamental tools of the trade for UX strategy work. If deconstruction allows us to critically examine assumptions by isolating individual components, synthesis allows us to explore new configurations for the whole.
5. DeconstructionBreaking observations down into component pieces. This is the classical definition of analysis.Breaking down research data into its component parts is a standard technique for analysis. Oneexample of deconstruction is turning an interview transcript into a series of separate comments oranswers to questions. Deconstruction is often used simply to prepare data for other analyticprocesses such as manipulation or summarization, or even abstraction.
5. Deconstruction The aim of deconstruction is to decouple each component so as to allow inspection of each in its own right. In other disciplines this process is used as a device for critical thinking, bypassing the potentially misleading image conveyed by the whole. In so doing deconstruction can be a powerful tool for exposing unquestioned assumptions about our users’ mental models or the business priorities of the client organization. Looking at our template analysis example, one of our first analysis tasks was to deconstruct the templates into their components. Like most of the technique we took a very low-tech approach to the task, blocking out the individual components with a pencil. In our case, the deconstruction made easier a lot of the subsequent analysis work.It was a minor, but significant, step in the overall process.
6. Summarization Collating similar observations together and treating them collectively. This is a standard technique in many quantitative analysis methods. The goal of summarizing data is to generate an additional set of data, typically more succinct, that encapsulates the raw data in some way. This may be a short sentence that captures the essential point from several minutes of an interview transcript: “participant finds site search unwieldy, confusing and difficult to use”.
6. Summarization We can also summarize the data quantitatively using summary or descriptive statistics such as frequencies, means, and standard deviations. Unlike the process of abstraction, where specificity is sacrificed for the sake of clarity; or aggregation, where several data sets are “rolled up”; summarization seeks to characterize the underlying data. Once again, spreadsheets are a very useful tool, especially when dealing with quantitative PostIt or Spreadsheets data. But they can be similarly useful when handling other data types. An equally useful medium for capturing summaries (once you have them) – particularly of qualitative data note sticky – is the PostIt or sticky note. This medium is also highly suited to manipulation and exploration of the resulting data. One advantage sticky notes have over a spreadsheet is that you can arrange and re-arrange them in two dimensions, so you can further manipulate and explore the summaries.
6. Summarization Index cards share many of the same advantages as sticky notes. They can be an excellent tool for capturing and working with summaries. They have the added advantage of being relatively robust and can therefore sustain a greater degree of handling. Index cards
Presenter: Huu Loca. Making Data Summariesb. Developing Categories And Coding Datac. Writing Theoretical Notesd. Quantificatione. Shaping metaphors
Review data immediately after they have been collected. Write a summary. The purpose of a summary is to provide easy access to the data later and to get an overview of what the data offer concerning the research question.
The summary might contain answers to the following questions: What is the context in which the data were collected? Why were they collected? Why is this particular situation? Why using this method of collection? What are the most important facts in the data? Is anything surprising? About which research issue are the data most informative? Do the data give rise to any new questions, points of view, suggestions, ideas? Do the data suggest what should be done next, in terms of further data collection, analysis, or action?Be sure to cross-reference each answer to the relevant passages in thedata (use a number counter for tape-recordings; use the number of theline in transcriptions, etc.)
We organize data into categories to gain "conceptual leverage" in presenting observations and conclusions. The categories need to be chosen from concepts which are relevant to the research question and which express the contents of the data.Two well recognized methods of coding data are: 1. The deductive method 2. The inductive method
In the deductive method, categories are chosen from the researcher using theoretical knowledge and the data is then searched for relevant passages. Development of categories are independent of the data.
In the inductive method, categories are chosen during and after scrutinizes the data. Categories are T derived U from the data from interesting, surprising or unexpected events--in relation to your research question.
Writing theoretical notes is appropriate at any stage in the research process. In writing, note any ideas or theories that come to mind relating to research question: what certain data mean, how facts could be explained, how an important concept could be defined, etc. Always date each note and label it with suitable catchword or keyword. For each entry, make a brief note of the data or event that prompted or gave rise to the idea.
Some elements of quantification are of great importance in people’s thinking. Quantification can be used in analyzing action research to carry out a preliminary survey and get some data quickly (e.g. number of student who participate—and demographics on students), to reveal researcher bias, or explore the generalizability of findings through statistics.
Metaphors transfer meaning (from one field ofexperience to another), generate meaning (e.g. labeldirects interaction), enrich the research process lookingfor metaphors widens the researchers horizons andenables the researcher to better understand the task athand), provide alternative approaches to reality, are goodat communicating complex matters, and come to mindnaturally during conversation.
Two activities should make up the critical methods: 1. Testing the findings and 2. Communicative validation These activities should be conducted only after findings are clearly formulated. In conducting critical analysis, the researcher must be open to data which question the theories upon which the research is based--and not just confirm them.
Write a series of sentences on cards, each expressing one important result of the analysis. Sort the sentences into sets according to issues to which they refer. Lay out each set of cards. Using photocopies, cut out any data which seem to relate to the sentence and place them beside the card. Compare data and sentence; then expand, modify, illustrate each sentence either by writing additional sentences and adding these to the layout of cards, or by rewriting the original card. This creates the "backbone" of a written report that will be rich in detail and grounded in the data.
Communicate interpretations to the participants (or to a critical friend who is familiar with the issues) and see if they agree. The amount of agreement indicates the validity of the results of the analysis.
CHOOSING A GRAPHPRESENTER: NGUYEN NGOC CAM
MAIN POINTS1. Purposes of data display2. Choosing a appropriate graph a. Line graph b. Bar graph c. Pie graph d. Scatter graph
PURPOSES OF DATA DISPLAY “A picture is worth a thousand words”: Graphs allow us to see the data and get a sense of what the data are saying about the individuals in the study. In general, pictures of data result in a more powerful message than tables With graphs we can find out important facts about the data you collected and use what you know to improve or change something.
PURPOSES OF DATA DISPLAY Graph by nature is a graphical presentation of data that collectively form into information that reflects the trend of some parameters. It shows the past, current and possibly the future (prediction). It allows users to see an overview of the relation between specific targets.
CHOOSING A APPROPRIATE GRAPH a. Line graph b. Bar graph c. Pie graph d. Scatter graph
LINE GRAPH A line graph can display continuous data over time, set against a common scale A line graph is best used when you wish to display a trend over a period of time.
Line graphs compare two variables. Each variable is plotted along an axis. A line graph has a vertical axis and a horizontal axis Some strengths of line graph: They are good at showing specific values of data, meaning that given one variable the other can easily be determined. They show trends in data clearly, meaning that they visibly show how one variable is affected by the other as it increases or decreases. They enable the viewer to make predictions about the results of data not yet recorded.
EXAMPLE If you have been giving a series of assessments along with implementing a new teaching strategy and you wish to show that achievement is slowly rising (or mistakes are decreasing) over time, a line graph would be appropriate WEEK 1 WEEK 2 WEEK 3 WEEK 4 WEEK 5 OLD 83 84 85 87 89STRATEGY NEW 82 85 89 92 94STRATEGY
EXAMPLE Average Test Scores 100 90 80 Percentage Correct 70 60 Control 50 Treatment 40 30 20 10 0 week 1 week 2 week 3 week 4 week 5 Weeks of Treatment
BAR GRAPH Bar graphs are a very common type of graph best suited for a qualitative independent variable. Since there is no uniform distance between levels of a qualitative variable, the discrete nature of the individual bars are well suited for this type of independent variable. Though you can extract trends between bars (e.g., they are gradually getting longer or shorter), you cannot calculate a slope from the heights of the bars. Bar graphs are best used whenever you are comparing two or more categorical variables.
EXAMPLE In the following table the Pre-test and Post-test scores of a group of students are displayed. PRE-TEST POST TEST STUDENT 1 75 84 STUDENT 2 88 99 STUDENT 3 90 82 STUDENT 4 63 80 STUDENT 5 85 97 STUDENT 6 79 89 STUDENT 7 94 100 STUDENT 8 83 80 STUDENT 9 88 90 STUDENT 10 68 86
EXAMPLE Sample Bar Graph 100 90 80 70 Percentage Correct 60 Pre-test 50 Post-test 40 30 20 10 0 Stu Stu Stu Stu Stu Stu Stu Stu Stu Stu den den den den den den den den den den t1 t2 t3 t4 t5 t6 t7 t8 t9 t 10 Students
Pie Chart Presenter: Ngan Giang
Definition A pie chart (or a circle graph) is a circular chart divided into sectors, illustrating proportion. Pie charts are perhaps the most popular chart type; they can be found in newspapers, business reports, and many other places.
Some criteria when using a pie chart Do the parts make up a meaningful whole? If not, use a different chart. Only use a pie chart if you can define the entire set in a way that makes sense to the viewer. Are the parts mutually exclusive? If there is overlap between the parts, use a different chart. How many parts do we have? If there are more than five to seven, use a different chart. Pie charts with lots of slices (or slices of very different size) are hard to read.
Do we want to compare the parts to each other or the parts to the whole? If the main purpose is to compare between the parts, use a different chart. The main purpose of the pie chart is to show part- whole relationships.=> In all other cases, do not use a pie chart, the bar chart is a much better choice for that. Using a pie chart requires a lot more thought, care, and awareness of its limitations than most other charts.
Definition A scatter diagram is a tool for analyzing relationships between two variables. One variable is plotted on the horizontal axis and the other is plotted on the vertical axis. Most often a scatter diagram is used to prove or disprove cause-and- effect relationships. While the diagram shows relationships, it does not by itself prove that one variable causes the other.
How to use it Collect data: Gather 50 to 100 paired samples of data that show a possible relationship. Draw the diagram: Label the axes in convenient multiples increasing on the horizontal axes from left to right and on the vertical axis from bottom to top. Label both axes.
Interpret the data: Scatter diagrams will generally show one of six possible correlations between the variables: Strong Positive Correlation : The value of Y clearly increases as the value of X increases.
Strong Negative Correlation: The value of Y clearly decreases as the value of X increases.
Weak Positive Correlation : The value of Y increases slightly as the value of X increases.
Weak Negative Correlation: The value of Y decreases slightly as the value of X increases.
Complex Correlation: The value of Y seems to be related to the value of X, but the relationship is not easily determined.
No Correlation: There is no demonstrated connection between the two variables.
Some Tips For Creating Graphs Usually, simpler is better Avoid using 3-D charts, they can be difficult to read and misleading. Choose color carefully, some colors can make portions of a chart look larger or smaller and can be misleading. Always give the chart a title, always label the axes, and provide a key if appropriate. In line graph and bar graph, the vertical scale must start at 0, if not, the graph is misleading
Average Test Scores Average Test Scores 96 100 90 94 80 92 Percentage Correct Percentage Correct 70 90 60 Control Control 50 88 Treatment Treatment 40 86 30 84 20 10 82 0 80 week 1 week 2 week 3 week 4 week 5 week 1 week 2 week 3 week 4 week 5 Weeks of Treatment Weeks of Treatment The vertical scale in graph 2 starts at 80 while the vertical scale in graph 1 starts at 0. The graph 2 is misleading because the percentage correct of control and treatment group appears to be very different, to be “far” from each other.